Overview

Dataset statistics

Number of variables44
Number of observations94815
Missing cells66107
Missing cells (%)1.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory31.8 MiB
Average record size in memory352.0 B

Variable types

Text1
Categorical26
Numeric17

Alerts

tipologia_imueble has constant value ""Constant
operacion has constant value ""Constant
ciudad has constant value ""Constant
a_reformar is highly overall correlated with buen_estadoHigh correlation
ano_construccion is highly overall correlated with cat_ano_construccion and 1 other fieldsHigh correlation
area_construida is highly overall correlated with n_banos and 2 other fieldsHigh correlation
ascensor is highly overall correlated with precioHigh correlation
buen_estado is highly overall correlated with a_reformarHigh correlation
cat_ano_construccion is highly overall correlated with ano_construccion and 1 other fieldsHigh correlation
cat_calidad is highly overall correlated with precio and 1 other fieldsHigh correlation
cat_n_max_pisos is highly overall correlated with cat_n_vecinosHigh correlation
cat_n_vecinos is highly overall correlated with cat_n_max_pisosHigh correlation
distancia_castellana is highly overall correlated with distancia_puerta_sol and 1 other fieldsHigh correlation
distancia_puerta_sol is highly overall correlated with ano_construccion and 3 other fieldsHigh correlation
jardin is highly overall correlated with piscinaHigh correlation
n_banos is highly overall correlated with area_construida and 2 other fieldsHigh correlation
n_habitaciones is highly overall correlated with area_construida and 1 other fieldsHigh correlation
parking is highly overall correlated with parking_incluido_precio and 1 other fieldsHigh correlation
parking_incluido_precio is highly overall correlated with parking and 1 other fieldsHigh correlation
piscina is highly overall correlated with jardin and 2 other fieldsHigh correlation
precio is highly overall correlated with area_construida and 4 other fieldsHigh correlation
precio_unitario_km2 is highly overall correlated with cat_calidad and 3 other fieldsHigh correlation
amueblado is highly imbalanced (75.5%)Imbalance
orientacion_n is highly imbalanced (50.7%)Imbalance
duplex is highly imbalanced (82.4%)Imbalance
estudio is highly imbalanced (81.8%)Imbalance
arico is highly imbalanced (84.1%)Imbalance
nueva_construccion is highly imbalanced (80.4%)Imbalance
ano_construccion has 55873 (58.9%) missing valuesMissing
n_piso has 3846 (4.1%) missing valuesMissing
exterior_interior has 6387 (6.7%) missing valuesMissing
precio_parking is highly skewed (γ1 = 52.12686995)Skewed
ano_construccion is highly skewed (γ1 = -22.48788092)Skewed
distancia_puerta_sol is highly skewed (γ1 = 27.74516836)Skewed
distancia_metro is highly skewed (γ1 = 227.8617291)Skewed
distancia_castellana is highly skewed (γ1 = 43.08790605)Skewed
n_habitaciones has 2745 (2.9%) zerosZeros
n_piso has 10112 (10.7%) zerosZeros

Reproduction

Analysis started2024-02-17 18:08:11.445722
Analysis finished2024-02-17 18:08:52.039892
Duration40.59 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

Distinct75804
Distinct (%)79.9%
Missing0
Missing (%)0.0%
Memory size740.9 KiB
2024-02-17T18:08:52.183202image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length21
Median length20
Mean length20.394104
Min length17

Characters and Unicode

Total characters1933667
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique61975 ?
Unique (%)65.4%

Sample

1st rowA15019136831406238029
2nd rowA6677225905472065344
3rd rowA13341979748618524775
4th rowA4775182175615276542
5th rowA2492087730711701973
ValueCountFrequency (%)
a5463639993615125363 11
 
< 0.1%
a14882068007191593522 9
 
< 0.1%
a2282202115281541721 9
 
< 0.1%
a1315840462730187222 8
 
< 0.1%
a6533487971225909396 7
 
< 0.1%
a9865685988976540204 7
 
< 0.1%
a17724009027760482208 7
 
< 0.1%
a6151307694369968367 7
 
< 0.1%
a14940791098683555615 7
 
< 0.1%
a15887017636239933239 7
 
< 0.1%
Other values (75794) 94736
99.9%
2024-02-17T18:08:52.441067image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 224648
11.6%
2 181752
9.4%
3 181563
9.4%
4 180886
9.4%
5 180745
9.3%
6 180516
9.3%
7 180378
9.3%
8 177716
9.2%
0 175751
9.1%
9 174897
9.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1838852
95.1%
Uppercase Letter 94815
 
4.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 224648
12.2%
2 181752
9.9%
3 181563
9.9%
4 180886
9.8%
5 180745
9.8%
6 180516
9.8%
7 180378
9.8%
8 177716
9.7%
0 175751
9.6%
9 174897
9.5%
Uppercase Letter
ValueCountFrequency (%)
A 94815
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1838852
95.1%
Latin 94815
 
4.9%

Most frequent character per script

Common
ValueCountFrequency (%)
1 224648
12.2%
2 181752
9.9%
3 181563
9.9%
4 180886
9.8%
5 180745
9.8%
6 180516
9.8%
7 180378
9.8%
8 177716
9.7%
0 175751
9.6%
9 174897
9.5%
Latin
ValueCountFrequency (%)
A 94815
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1933667
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 224648
11.6%
2 181752
9.4%
3 181563
9.4%
4 180886
9.4%
5 180745
9.3%
6 180516
9.3%
7 180378
9.3%
8 177716
9.2%
0 175751
9.1%
9 174897
9.0%

fecha
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size740.9 KiB
201812
44270 
201803
21920 
201809
15973 
201806
12652 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters568890
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row201803
2nd row201803
3rd row201803
4th row201803
5th row201803

Common Values

ValueCountFrequency (%)
201812 44270
46.7%
201803 21920
23.1%
201809 15973
 
16.8%
201806 12652
 
13.3%

Length

2024-02-17T18:08:52.557562image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T18:08:52.632073image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
201812 44270
46.7%
201803 21920
23.1%
201809 15973
 
16.8%
201806 12652
 
13.3%

Most occurring characters

ValueCountFrequency (%)
0 145360
25.6%
2 139085
24.4%
1 139085
24.4%
8 94815
16.7%
3 21920
 
3.9%
9 15973
 
2.8%
6 12652
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 568890
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 145360
25.6%
2 139085
24.4%
1 139085
24.4%
8 94815
16.7%
3 21920
 
3.9%
9 15973
 
2.8%
6 12652
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
Common 568890
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 145360
25.6%
2 139085
24.4%
1 139085
24.4%
8 94815
16.7%
3 21920
 
3.9%
9 15973
 
2.8%
6 12652
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 568890
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 145360
25.6%
2 139085
24.4%
1 139085
24.4%
8 94815
16.7%
3 21920
 
3.9%
9 15973
 
2.8%
6 12652
 
2.2%

precio
Real number (ℝ)

HIGH CORRELATION 

Distinct2761
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean396110.11
Minimum21000
Maximum8133000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size740.9 KiB
2024-02-17T18:08:52.757763image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum21000
5-th percentile97000
Q1160000
median262000
Q3467000
95-th percentile1135000
Maximum8133000
Range8112000
Interquartile range (IQR)307000

Descriptive statistics

Standard deviation417074.41
Coefficient of variation (CV)1.0529254
Kurtosis28.98006
Mean396110.11
Median Absolute Deviation (MAD)124000
Skewness4.0388853
Sum3.755718 × 1010
Variance1.7395106 × 1011
MonotonicityNot monotonic
2024-02-17T18:08:52.873784image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
137000 448
 
0.5%
127000 416
 
0.4%
128000 411
 
0.4%
132000 400
 
0.4%
130000 391
 
0.4%
158000 378
 
0.4%
138000 377
 
0.4%
147000 376
 
0.4%
162000 369
 
0.4%
157000 368
 
0.4%
Other values (2751) 90881
95.9%
ValueCountFrequency (%)
21000 1
 
< 0.1%
24000 3
< 0.1%
25000 1
 
< 0.1%
26000 1
 
< 0.1%
28000 1
 
< 0.1%
29000 3
< 0.1%
30000 1
 
< 0.1%
32000 1
 
< 0.1%
33000 3
< 0.1%
34000 1
 
< 0.1%
ValueCountFrequency (%)
8133000 1
< 0.1%
7138000 1
< 0.1%
7124000 1
< 0.1%
7044000 1
< 0.1%
7018000 1
< 0.1%
6996000 1
< 0.1%
6970000 1
< 0.1%
6848000 1
< 0.1%
6829000 1
< 0.1%
6729000 1
< 0.1%

precio_unitario_km2
Real number (ℝ)

HIGH CORRELATION 

Distinct31151
Distinct (%)32.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3661.0515
Minimum805.30973
Maximum9997.561
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size740.9 KiB
2024-02-17T18:08:52.981655image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum805.30973
5-th percentile1426.6667
Q12240
median3480
Q34744.623
95-th percentile6771.4286
Maximum9997.561
Range9192.2512
Interquartile range (IQR)2504.623

Descriptive statistics

Standard deviation1700.4994
Coefficient of variation (CV)0.46448387
Kurtosis0.23351824
Mean3661.0515
Median Absolute Deviation (MAD)1250.1587
Skewness0.72169916
Sum3.471226 × 108
Variance2891698.2
MonotonicityNot monotonic
2024-02-17T18:08:53.097413image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2000 373
 
0.4%
3000 236
 
0.2%
5000 211
 
0.2%
4000 201
 
0.2%
2200 161
 
0.2%
1500 158
 
0.2%
1800 149
 
0.2%
3500 148
 
0.2%
2500 140
 
0.1%
2600 131
 
0.1%
Other values (31141) 92907
98.0%
ValueCountFrequency (%)
805.3097345 1
< 0.1%
805.5555556 2
< 0.1%
805.9701493 1
< 0.1%
806.4516129 1
< 0.1%
807.0175439 1
< 0.1%
808.2191781 1
< 0.1%
808.3333333 1
< 0.1%
808.8235294 1
< 0.1%
809.0909091 1
< 0.1%
810.5263158 1
< 0.1%
ValueCountFrequency (%)
9997.560976 1
< 0.1%
9994.285714 1
< 0.1%
9993.377483 1
< 0.1%
9992.248062 1
< 0.1%
9991.150442 1
< 0.1%
9990.909091 1
< 0.1%
9984.924623 1
< 0.1%
9979.310345 1
< 0.1%
9975 2
< 0.1%
9973.913043 1
< 0.1%

tipologia_imueble
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size740.9 KiB
HOME
94815 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters379260
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHOME
2nd rowHOME
3rd rowHOME
4th rowHOME
5th rowHOME

Common Values

ValueCountFrequency (%)
HOME 94815
100.0%

Length

2024-02-17T18:08:53.181885image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T18:08:53.242374image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
home 94815
100.0%

Most occurring characters

ValueCountFrequency (%)
H 94815
25.0%
O 94815
25.0%
M 94815
25.0%
E 94815
25.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 379260
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
H 94815
25.0%
O 94815
25.0%
M 94815
25.0%
E 94815
25.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 379260
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
H 94815
25.0%
O 94815
25.0%
M 94815
25.0%
E 94815
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 379260
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
H 94815
25.0%
O 94815
25.0%
M 94815
25.0%
E 94815
25.0%

operacion
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size740.9 KiB
SALE
94815 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters379260
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSALE
2nd rowSALE
3rd rowSALE
4th rowSALE
5th rowSALE

Common Values

ValueCountFrequency (%)
SALE 94815
100.0%

Length

2024-02-17T18:08:53.316714image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T18:08:53.388351image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
sale 94815
100.0%

Most occurring characters

ValueCountFrequency (%)
S 94815
25.0%
A 94815
25.0%
L 94815
25.0%
E 94815
25.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 379260
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 94815
25.0%
A 94815
25.0%
L 94815
25.0%
E 94815
25.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 379260
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 94815
25.0%
A 94815
25.0%
L 94815
25.0%
E 94815
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 379260
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 94815
25.0%
A 94815
25.0%
L 94815
25.0%
E 94815
25.0%

area_construida
Real number (ℝ)

HIGH CORRELATION 

Distinct558
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean101.39716
Minimum21
Maximum985
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size740.9 KiB
2024-02-17T18:08:53.464028image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile40
Q162
median83
Q3117
95-th percentile225
Maximum985
Range964
Interquartile range (IQR)55

Descriptive statistics

Standard deviation67.078259
Coefficient of variation (CV)0.66153981
Kurtosis17.634481
Mean101.39716
Median Absolute Deviation (MAD)25
Skewness3.1778064
Sum9613972
Variance4499.4929
MonotonicityNot monotonic
2024-02-17T18:08:53.584758image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 3129
 
3.3%
70 2976
 
3.1%
80 2606
 
2.7%
65 2418
 
2.6%
75 2350
 
2.5%
90 2158
 
2.3%
50 1970
 
2.1%
100 1931
 
2.0%
55 1745
 
1.8%
110 1576
 
1.7%
Other values (548) 71956
75.9%
ValueCountFrequency (%)
21 58
 
0.1%
22 61
 
0.1%
23 54
 
0.1%
24 74
 
0.1%
25 235
 
0.2%
26 75
 
0.1%
27 127
 
0.1%
28 158
 
0.2%
29 75
 
0.1%
30 652
0.7%
ValueCountFrequency (%)
985 1
< 0.1%
982 1
< 0.1%
951 1
< 0.1%
950 1
< 0.1%
941 2
< 0.1%
934 1
< 0.1%
928 1
< 0.1%
926 1
< 0.1%
922 1
< 0.1%
900 2
< 0.1%

n_habitaciones
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5808996
Minimum0
Maximum93
Zeros2745
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size740.9 KiB
2024-02-17T18:08:53.683882image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q33
95-th percentile4
Maximum93
Range93
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2448777
Coefficient of variation (CV)0.48234256
Kurtosis301.56797
Mean2.5808996
Median Absolute Deviation (MAD)1
Skewness4.9738445
Sum244708
Variance1.5497206
MonotonicityNot monotonic
2024-02-17T18:08:53.770428image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
3 33961
35.8%
2 28422
30.0%
1 13338
 
14.1%
4 11674
 
12.3%
5 3350
 
3.5%
0 2745
 
2.9%
6 789
 
0.8%
7 279
 
0.3%
8 142
 
0.1%
9 35
 
< 0.1%
Other values (11) 80
 
0.1%
ValueCountFrequency (%)
0 2745
 
2.9%
1 13338
 
14.1%
2 28422
30.0%
3 33961
35.8%
4 11674
 
12.3%
5 3350
 
3.5%
6 789
 
0.8%
7 279
 
0.3%
8 142
 
0.1%
9 35
 
< 0.1%
ValueCountFrequency (%)
93 1
 
< 0.1%
33 1
 
< 0.1%
20 2
 
< 0.1%
18 2
 
< 0.1%
16 2
 
< 0.1%
15 3
 
< 0.1%
14 5
 
< 0.1%
13 6
 
< 0.1%
12 18
< 0.1%
11 16
< 0.1%

n_banos
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5854559
Minimum0
Maximum20
Zeros89
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size740.9 KiB
2024-02-17T18:08:53.850569image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum20
Range20
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.84302421
Coefficient of variation (CV)0.53172353
Kurtosis14.881077
Mean1.5854559
Median Absolute Deviation (MAD)0
Skewness2.4345135
Sum150325
Variance0.71068981
MonotonicityNot monotonic
2024-02-17T18:08:54.228270image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1 53583
56.5%
2 31352
33.1%
3 6673
 
7.0%
4 2132
 
2.2%
5 722
 
0.8%
6 159
 
0.2%
0 89
 
0.1%
7 39
 
< 0.1%
8 28
 
< 0.1%
11 15
 
< 0.1%
Other values (8) 23
 
< 0.1%
ValueCountFrequency (%)
0 89
 
0.1%
1 53583
56.5%
2 31352
33.1%
3 6673
 
7.0%
4 2132
 
2.2%
5 722
 
0.8%
6 159
 
0.2%
7 39
 
< 0.1%
8 28
 
< 0.1%
9 7
 
< 0.1%
ValueCountFrequency (%)
20 1
 
< 0.1%
18 1
 
< 0.1%
16 1
 
< 0.1%
14 2
 
< 0.1%
13 2
 
< 0.1%
12 2
 
< 0.1%
11 15
< 0.1%
10 7
 
< 0.1%
9 7
 
< 0.1%
8 28
< 0.1%

terraza
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size740.9 KiB
0
61131 
1
33684 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters94815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 61131
64.5%
1 33684
35.5%

Length

2024-02-17T18:08:54.315183image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T18:08:54.383818image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 61131
64.5%
1 33684
35.5%

Most occurring characters

ValueCountFrequency (%)
0 61131
64.5%
1 33684
35.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 94815
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 61131
64.5%
1 33684
35.5%

Most occurring scripts

ValueCountFrequency (%)
Common 94815
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 61131
64.5%
1 33684
35.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 94815
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 61131
64.5%
1 33684
35.5%

ascensor
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size740.9 KiB
1
65953 
0
28862 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters94815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 65953
69.6%
0 28862
30.4%

Length

2024-02-17T18:08:54.464253image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T18:08:54.533674image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 65953
69.6%
0 28862
30.4%

Most occurring characters

ValueCountFrequency (%)
1 65953
69.6%
0 28862
30.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 94815
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 65953
69.6%
0 28862
30.4%

Most occurring scripts

ValueCountFrequency (%)
Common 94815
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 65953
69.6%
0 28862
30.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 94815
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 65953
69.6%
0 28862
30.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size740.9 KiB
0
52281 
1
42534 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters94815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 52281
55.1%
1 42534
44.9%

Length

2024-02-17T18:08:54.599986image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T18:08:54.660960image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 52281
55.1%
1 42534
44.9%

Most occurring characters

ValueCountFrequency (%)
0 52281
55.1%
1 42534
44.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 94815
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 52281
55.1%
1 42534
44.9%

Most occurring scripts

ValueCountFrequency (%)
Common 94815
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 52281
55.1%
1 42534
44.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 94815
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 52281
55.1%
1 42534
44.9%

amueblado
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size740.9 KiB
3
88799 
2
 
4699
1
 
1317

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters94815
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 88799
93.7%
2 4699
 
5.0%
1 1317
 
1.4%

Length

2024-02-17T18:08:54.745587image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T18:08:54.816549image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
3 88799
93.7%
2 4699
 
5.0%
1 1317
 
1.4%

Most occurring characters

ValueCountFrequency (%)
3 88799
93.7%
2 4699
 
5.0%
1 1317
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 94815
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 88799
93.7%
2 4699
 
5.0%
1 1317
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Common 94815
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 88799
93.7%
2 4699
 
5.0%
1 1317
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 94815
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 88799
93.7%
2 4699
 
5.0%
1 1317
 
1.4%

parking
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size740.9 KiB
0
73472 
1
21343 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters94815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 73472
77.5%
1 21343
 
22.5%

Length

2024-02-17T18:08:54.877093image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T18:08:54.946451image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 73472
77.5%
1 21343
 
22.5%

Most occurring characters

ValueCountFrequency (%)
0 73472
77.5%
1 21343
 
22.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 94815
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 73472
77.5%
1 21343
 
22.5%

Most occurring scripts

ValueCountFrequency (%)
Common 94815
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 73472
77.5%
1 21343
 
22.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 94815
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 73472
77.5%
1 21343
 
22.5%

parking_incluido_precio
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size740.9 KiB
0
73472 
1
21343 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters94815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 73472
77.5%
1 21343
 
22.5%

Length

2024-02-17T18:08:55.017569image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T18:08:55.081208image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 73472
77.5%
1 21343
 
22.5%

Most occurring characters

ValueCountFrequency (%)
0 73472
77.5%
1 21343
 
22.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 94815
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 73472
77.5%
1 21343
 
22.5%

Most occurring scripts

ValueCountFrequency (%)
Common 94815
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 73472
77.5%
1 21343
 
22.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 94815
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 73472
77.5%
1 21343
 
22.5%

precio_parking
Real number (ℝ)

SKEWED 

Distinct146
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean719.87267
Minimum1
Maximum925001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size740.9 KiB
2024-02-17T18:08:55.180101image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum925001
Range925000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7513.8764
Coefficient of variation (CV)10.437785
Kurtosis5060.2622
Mean719.87267
Median Absolute Deviation (MAD)0
Skewness52.12687
Sum68254727
Variance56458338
MonotonicityNot monotonic
2024-02-17T18:08:55.297917image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 92624
97.7%
20001 231
 
0.2%
30001 210
 
0.2%
25001 186
 
0.2%
15001 183
 
0.2%
40001 141
 
0.1%
50001 136
 
0.1%
45001 87
 
0.1%
35001 76
 
0.1%
60001 62
 
0.1%
Other values (136) 879
 
0.9%
ValueCountFrequency (%)
1 92624
97.7%
2 9
 
< 0.1%
6 2
 
< 0.1%
7 1
 
< 0.1%
11 2
 
< 0.1%
16 1
 
< 0.1%
17 1
 
< 0.1%
26 3
 
< 0.1%
41 4
 
< 0.1%
51 5
 
< 0.1%
ValueCountFrequency (%)
925001 1
 
< 0.1%
770001 1
 
< 0.1%
750001 1
 
< 0.1%
510001 1
 
< 0.1%
450001 1
 
< 0.1%
275001 1
 
< 0.1%
250001 1
 
< 0.1%
231001 2
< 0.1%
220001 1
 
< 0.1%
150001 3
< 0.1%

orientacion_n
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size740.9 KiB
0
84592 
1
10223 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters94815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 84592
89.2%
1 10223
 
10.8%

Length

2024-02-17T18:08:55.396740image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T18:08:55.465041image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 84592
89.2%
1 10223
 
10.8%

Most occurring characters

ValueCountFrequency (%)
0 84592
89.2%
1 10223
 
10.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 94815
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 84592
89.2%
1 10223
 
10.8%

Most occurring scripts

ValueCountFrequency (%)
Common 94815
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 84592
89.2%
1 10223
 
10.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 94815
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 84592
89.2%
1 10223
 
10.8%

orientacion_s
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size740.9 KiB
0
72427 
1
22388 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters94815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 72427
76.4%
1 22388
 
23.6%

Length

2024-02-17T18:08:55.550077image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T18:08:55.613401image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 72427
76.4%
1 22388
 
23.6%

Most occurring characters

ValueCountFrequency (%)
0 72427
76.4%
1 22388
 
23.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 94815
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 72427
76.4%
1 22388
 
23.6%

Most occurring scripts

ValueCountFrequency (%)
Common 94815
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 72427
76.4%
1 22388
 
23.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 94815
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 72427
76.4%
1 22388
 
23.6%

orientacion_e
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size740.9 KiB
0
75602 
1
19213 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters94815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 75602
79.7%
1 19213
 
20.3%

Length

2024-02-17T18:08:55.682158image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T18:08:55.751242image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 75602
79.7%
1 19213
 
20.3%

Most occurring characters

ValueCountFrequency (%)
0 75602
79.7%
1 19213
 
20.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 94815
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 75602
79.7%
1 19213
 
20.3%

Most occurring scripts

ValueCountFrequency (%)
Common 94815
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 75602
79.7%
1 19213
 
20.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 94815
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 75602
79.7%
1 19213
 
20.3%

orientacion_o
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size740.9 KiB
0
80739 
1
14076 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters94815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 80739
85.2%
1 14076
 
14.8%

Length

2024-02-17T18:08:55.830829image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T18:08:55.897596image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 80739
85.2%
1 14076
 
14.8%

Most occurring characters

ValueCountFrequency (%)
0 80739
85.2%
1 14076
 
14.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 94815
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 80739
85.2%
1 14076
 
14.8%

Most occurring scripts

ValueCountFrequency (%)
Common 94815
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 80739
85.2%
1 14076
 
14.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 94815
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 80739
85.2%
1 14076
 
14.8%

trastero
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size740.9 KiB
0
70272 
1
24543 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters94815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 70272
74.1%
1 24543
 
25.9%

Length

2024-02-17T18:08:55.963445image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T18:08:56.033766image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 70272
74.1%
1 24543
 
25.9%

Most occurring characters

ValueCountFrequency (%)
0 70272
74.1%
1 24543
 
25.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 94815
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 70272
74.1%
1 24543
 
25.9%

Most occurring scripts

ValueCountFrequency (%)
Common 94815
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 70272
74.1%
1 24543
 
25.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 94815
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 70272
74.1%
1 24543
 
25.9%

armarios
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size740.9 KiB
1
54235 
0
40580 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters94815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 54235
57.2%
0 40580
42.8%

Length

2024-02-17T18:08:56.099059image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T18:08:56.178811image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 54235
57.2%
0 40580
42.8%

Most occurring characters

ValueCountFrequency (%)
1 54235
57.2%
0 40580
42.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 94815
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 54235
57.2%
0 40580
42.8%

Most occurring scripts

ValueCountFrequency (%)
Common 94815
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 54235
57.2%
0 40580
42.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 94815
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 54235
57.2%
0 40580
42.8%

piscina
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size740.9 KiB
0
80767 
1
14048 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters94815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 80767
85.2%
1 14048
 
14.8%

Length

2024-02-17T18:08:56.246377image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T18:08:56.315294image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 80767
85.2%
1 14048
 
14.8%

Most occurring characters

ValueCountFrequency (%)
0 80767
85.2%
1 14048
 
14.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 94815
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 80767
85.2%
1 14048
 
14.8%

Most occurring scripts

ValueCountFrequency (%)
Common 94815
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 80767
85.2%
1 14048
 
14.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 94815
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 80767
85.2%
1 14048
 
14.8%

portero
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size740.9 KiB
0
71151 
1
23664 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters94815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 71151
75.0%
1 23664
 
25.0%

Length

2024-02-17T18:08:56.378849image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T18:08:56.446326image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 71151
75.0%
1 23664
 
25.0%

Most occurring characters

ValueCountFrequency (%)
0 71151
75.0%
1 23664
 
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 94815
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 71151
75.0%
1 23664
 
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 94815
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 71151
75.0%
1 23664
 
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 94815
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 71151
75.0%
1 23664
 
25.0%

jardin
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size740.9 KiB
0
77319 
1
17496 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters94815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 77319
81.5%
1 17496
 
18.5%

Length

2024-02-17T18:08:56.530924image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T18:08:56.598078image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 77319
81.5%
1 17496
 
18.5%

Most occurring characters

ValueCountFrequency (%)
0 77319
81.5%
1 17496
 
18.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 94815
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 77319
81.5%
1 17496
 
18.5%

Most occurring scripts

ValueCountFrequency (%)
Common 94815
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 77319
81.5%
1 17496
 
18.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 94815
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 77319
81.5%
1 17496
 
18.5%

duplex
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size740.9 KiB
0
92313 
1
 
2502

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters94815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 92313
97.4%
1 2502
 
2.6%

Length

2024-02-17T18:08:56.664391image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T18:08:56.732933image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 92313
97.4%
1 2502
 
2.6%

Most occurring characters

ValueCountFrequency (%)
0 92313
97.4%
1 2502
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 94815
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 92313
97.4%
1 2502
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Common 94815
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 92313
97.4%
1 2502
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 94815
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 92313
97.4%
1 2502
 
2.6%

estudio
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size740.9 KiB
0
92203 
1
 
2612

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters94815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 92203
97.2%
1 2612
 
2.8%

Length

2024-02-17T18:08:56.796542image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T18:08:56.864790image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 92203
97.2%
1 2612
 
2.8%

Most occurring characters

ValueCountFrequency (%)
0 92203
97.2%
1 2612
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 94815
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 92203
97.2%
1 2612
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
Common 94815
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 92203
97.2%
1 2612
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 94815
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 92203
97.2%
1 2612
 
2.8%

arico
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size740.9 KiB
0
92619 
1
 
2196

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters94815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 92619
97.7%
1 2196
 
2.3%

Length

2024-02-17T18:08:56.930743image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T18:08:56.996233image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 92619
97.7%
1 2196
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 92619
97.7%
1 2196
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 94815
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 92619
97.7%
1 2196
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Common 94815
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 92619
97.7%
1 2196
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 94815
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 92619
97.7%
1 2196
 
2.3%

ano_construccion
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct191
Distinct (%)0.5%
Missing55873
Missing (%)58.9%
Infinite0
Infinite (%)0.0%
Mean1964.6935
Minimum1
Maximum2291
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size740.9 KiB
2024-02-17T18:08:57.080950image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1900
Q11955
median1968
Q31987
95-th percentile2008
Maximum2291
Range2290
Interquartile range (IQR)32

Descriptive statistics

Standard deviation55.891001
Coefficient of variation (CV)0.028447695
Kurtosis736.98528
Mean1964.6935
Median Absolute Deviation (MAD)16
Skewness-22.487881
Sum76509094
Variance3123.8039
MonotonicityNot monotonic
2024-02-17T18:08:57.182135image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1960 2313
 
2.4%
1970 2095
 
2.2%
1965 1825
 
1.9%
1900 1643
 
1.7%
1950 862
 
0.9%
1930 822
 
0.9%
1966 784
 
0.8%
1940 782
 
0.8%
2006 742
 
0.8%
1975 741
 
0.8%
Other values (181) 26333
27.8%
(Missing) 55873
58.9%
ValueCountFrequency (%)
1 1
 
< 0.1%
7 1
 
< 0.1%
10 2
< 0.1%
19 3
< 0.1%
48 1
 
< 0.1%
49 2
< 0.1%
50 2
< 0.1%
54 1
 
< 0.1%
160 1
 
< 0.1%
173 1
 
< 0.1%
ValueCountFrequency (%)
2291 1
 
< 0.1%
2028 1
 
< 0.1%
2020 13
 
< 0.1%
2019 114
 
0.1%
2018 410
0.4%
2017 190
0.2%
2016 52
 
0.1%
2015 69
 
0.1%
2014 54
 
0.1%
2013 65
 
0.1%

n_piso
Real number (ℝ)

MISSING  ZEROS 

Distinct13
Distinct (%)< 0.1%
Missing3846
Missing (%)4.1%
Infinite0
Infinite (%)0.0%
Mean2.7479031
Minimum-1
Maximum11
Zeros10112
Zeros (%)10.7%
Negative936
Negative (%)1.0%
Memory size740.9 KiB
2024-02-17T18:08:57.275694image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q11
median2
Q34
95-th percentile7
Maximum11
Range12
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.2575362
Coefficient of variation (CV)0.82154869
Kurtosis1.5967542
Mean2.7479031
Median Absolute Deviation (MAD)1
Skewness1.1589046
Sum249974
Variance5.0964698
MonotonicityNot monotonic
2024-02-17T18:08:57.347487image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 20336
21.4%
2 17044
18.0%
3 14754
15.6%
4 11635
12.3%
0 10112
10.7%
5 6162
 
6.5%
6 3733
 
3.9%
7 2447
 
2.6%
8 1495
 
1.6%
11 1051
 
1.1%
Other values (3) 2200
 
2.3%
(Missing) 3846
 
4.1%
ValueCountFrequency (%)
-1 936
 
1.0%
0 10112
10.7%
1 20336
21.4%
2 17044
18.0%
3 14754
15.6%
4 11635
12.3%
5 6162
 
6.5%
6 3733
 
3.9%
7 2447
 
2.6%
8 1495
 
1.6%
ValueCountFrequency (%)
11 1051
 
1.1%
10 450
 
0.5%
9 814
 
0.9%
8 1495
 
1.6%
7 2447
 
2.6%
6 3733
 
3.9%
5 6162
 
6.5%
4 11635
12.3%
3 14754
15.6%
2 17044
18.0%

exterior_interior
Categorical

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing6387
Missing (%)6.7%
Memory size740.9 KiB
1.0
76324 
2.0
12104 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters265284
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row2.0
3rd row1.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 76324
80.5%
2.0 12104
 
12.8%
(Missing) 6387
 
6.7%

Length

2024-02-17T18:08:57.448496image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T18:08:57.531567image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 76324
86.3%
2.0 12104
 
13.7%

Most occurring characters

ValueCountFrequency (%)
. 88428
33.3%
0 88428
33.3%
1 76324
28.8%
2 12104
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176856
66.7%
Other Punctuation 88428
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 88428
50.0%
1 76324
43.2%
2 12104
 
6.8%
Other Punctuation
ValueCountFrequency (%)
. 88428
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 265284
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 88428
33.3%
0 88428
33.3%
1 76324
28.8%
2 12104
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 265284
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 88428
33.3%
0 88428
33.3%
1 76324
28.8%
2 12104
 
4.6%

cat_ano_construccion
Real number (ℝ)

HIGH CORRELATION 

Distinct168
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1965.7037
Minimum1623
Maximum2018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size740.9 KiB
2024-02-17T18:08:57.615196image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1623
5-th percentile1900
Q11955
median1967
Q31984
95-th percentile2008
Maximum2018
Range395
Interquartile range (IQR)29

Descriptive statistics

Standard deviation29.113566
Coefficient of variation (CV)0.01481076
Kurtosis2.0891814
Mean1965.7037
Median Absolute Deviation (MAD)14
Skewness-0.82024174
Sum1.863782 × 108
Variance847.59974
MonotonicityNot monotonic
2024-02-17T18:08:57.731820image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1960 6028
 
6.4%
1965 4929
 
5.2%
1970 4687
 
4.9%
1900 3443
 
3.6%
1950 2139
 
2.3%
1940 2037
 
2.1%
1969 1978
 
2.1%
1968 1890
 
2.0%
1930 1845
 
1.9%
1966 1818
 
1.9%
Other values (158) 64021
67.5%
ValueCountFrequency (%)
1623 2
 
< 0.1%
1627 3
 
< 0.1%
1655 1
 
< 0.1%
1692 1
 
< 0.1%
1696 1
 
< 0.1%
1700 1
 
< 0.1%
1723 1
 
< 0.1%
1730 2
 
< 0.1%
1780 1
 
< 0.1%
1800 8
< 0.1%
ValueCountFrequency (%)
2018 963
1.0%
2017 432
0.5%
2016 146
 
0.2%
2015 181
 
0.2%
2014 383
 
0.4%
2013 171
 
0.2%
2012 189
 
0.2%
2011 182
 
0.2%
2010 427
0.5%
2009 592
0.6%

cat_n_max_pisos
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.37647
Minimum0
Maximum26
Zeros101
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size740.9 KiB
2024-02-17T18:08:57.847539image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q15
median6
Q38
95-th percentile12
Maximum26
Range26
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.8454968
Coefficient of variation (CV)0.44624953
Kurtosis5.8429441
Mean6.37647
Median Absolute Deviation (MAD)1
Skewness1.7681567
Sum604585
Variance8.0968518
MonotonicityNot monotonic
2024-02-17T18:08:57.953634image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
5 21398
22.6%
4 13995
14.8%
7 13665
14.4%
6 13557
14.3%
8 9390
9.9%
3 5447
 
5.7%
9 5160
 
5.4%
10 2850
 
3.0%
2 1778
 
1.9%
11 1661
 
1.8%
Other values (16) 5914
 
6.2%
ValueCountFrequency (%)
0 101
 
0.1%
1 588
 
0.6%
2 1778
 
1.9%
3 5447
 
5.7%
4 13995
14.8%
5 21398
22.6%
6 13557
14.3%
7 13665
14.4%
8 9390
9.9%
9 5160
 
5.4%
ValueCountFrequency (%)
26 46
 
< 0.1%
25 31
 
< 0.1%
23 88
 
0.1%
22 72
 
0.1%
21 182
0.2%
20 87
 
0.1%
19 20
 
< 0.1%
18 83
 
0.1%
17 287
0.3%
16 248
0.3%

cat_n_vecinos
Real number (ℝ)

HIGH CORRELATION 

Distinct329
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.187164
Minimum1
Maximum1499
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size740.9 KiB
2024-02-17T18:08:58.034896image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q112
median21
Q340
95-th percentile141
Maximum1499
Range1498
Interquartile range (IQR)28

Descriptive statistics

Standard deviation54.254387
Coefficient of variation (CV)1.3844938
Kurtosis35.256411
Mean39.187164
Median Absolute Deviation (MAD)11
Skewness4.383168
Sum3715531
Variance2943.5385
MonotonicityNot monotonic
2024-02-17T18:08:58.148492image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 4863
 
5.1%
21 4096
 
4.3%
9 3965
 
4.2%
13 3334
 
3.5%
17 2875
 
3.0%
16 2746
 
2.9%
7 2523
 
2.7%
15 2464
 
2.6%
10 2421
 
2.6%
14 2392
 
2.5%
Other values (319) 63136
66.6%
ValueCountFrequency (%)
1 691
 
0.7%
2 1087
 
1.1%
3 1126
 
1.2%
4 1402
 
1.5%
5 1477
 
1.6%
6 1107
 
1.2%
7 2523
2.7%
8 1577
 
1.7%
9 3965
4.2%
10 2421
2.6%
ValueCountFrequency (%)
1499 2
 
< 0.1%
724 23
< 0.1%
701 1
 
< 0.1%
638 4
 
< 0.1%
574 55
0.1%
518 2
 
< 0.1%
512 1
 
< 0.1%
503 12
 
< 0.1%
501 10
 
< 0.1%
478 22
 
< 0.1%

cat_calidad
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean4.8521843
Minimum0
Maximum9
Zeros377
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size740.9 KiB
2024-02-17T18:08:58.232210image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q14
median5
Q36
95-th percentile7
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4629876
Coefficient of variation (CV)0.30151113
Kurtosis-0.050457092
Mean4.8521843
Median Absolute Deviation (MAD)1
Skewness-0.0355007
Sum460055
Variance2.1403327
MonotonicityNot monotonic
2024-02-17T18:08:58.306486image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
4 24673
26.0%
5 20725
21.9%
6 20491
21.6%
3 12634
13.3%
7 10428
11.0%
2 2703
 
2.9%
8 1527
 
1.6%
1 629
 
0.7%
9 627
 
0.7%
0 377
 
0.4%
(Missing) 1
 
< 0.1%
ValueCountFrequency (%)
0 377
 
0.4%
1 629
 
0.7%
2 2703
 
2.9%
3 12634
13.3%
4 24673
26.0%
5 20725
21.9%
6 20491
21.6%
7 10428
11.0%
8 1527
 
1.6%
9 627
 
0.7%
ValueCountFrequency (%)
9 627
 
0.7%
8 1527
 
1.6%
7 10428
11.0%
6 20491
21.6%
5 20725
21.9%
4 24673
26.0%
3 12634
13.3%
2 2703
 
2.9%
1 629
 
0.7%
0 377
 
0.4%

nueva_construccion
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size740.9 KiB
0
91933 
1
 
2882

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters94815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 91933
97.0%
1 2882
 
3.0%

Length

2024-02-17T18:08:58.395612image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T18:08:58.447845image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 91933
97.0%
1 2882
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 91933
97.0%
1 2882
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 94815
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 91933
97.0%
1 2882
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common 94815
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 91933
97.0%
1 2882
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 94815
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 91933
97.0%
1 2882
 
3.0%

a_reformar
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size740.9 KiB
0
77126 
1
17689 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters94815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 77126
81.3%
1 17689
 
18.7%

Length

2024-02-17T18:08:58.533018image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T18:08:58.595774image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 77126
81.3%
1 17689
 
18.7%

Most occurring characters

ValueCountFrequency (%)
0 77126
81.3%
1 17689
 
18.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 94815
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 77126
81.3%
1 17689
 
18.7%

Most occurring scripts

ValueCountFrequency (%)
Common 94815
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 77126
81.3%
1 17689
 
18.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 94815
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 77126
81.3%
1 17689
 
18.7%

buen_estado
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size740.9 KiB
1
74244 
0
20571 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters94815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 74244
78.3%
0 20571
 
21.7%

Length

2024-02-17T18:08:58.664723image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T18:08:58.732816image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 74244
78.3%
0 20571
 
21.7%

Most occurring characters

ValueCountFrequency (%)
1 74244
78.3%
0 20571
 
21.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 94815
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 74244
78.3%
0 20571
 
21.7%

Most occurring scripts

ValueCountFrequency (%)
Common 94815
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 74244
78.3%
0 20571
 
21.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 94815
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 74244
78.3%
0 20571
 
21.7%

distancia_puerta_sol
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct94713
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4862695
Minimum0.0076465716
Maximum415.75258
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size740.9 KiB
2024-02-17T18:08:58.812608image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0.0076465716
5-th percentile0.75998548
Q12.407711
median4.1235046
Q36.2134633
95-th percentile9.1800493
Maximum415.75258
Range415.74494
Interquartile range (IQR)3.8057523

Descriptive statistics

Standard deviation2.994906
Coefficient of variation (CV)0.66757159
Kurtosis3749.5623
Mean4.4862695
Median Absolute Deviation (MAD)1.8399367
Skewness27.745168
Sum425365.64
Variance8.9694621
MonotonicityNot monotonic
2024-02-17T18:08:58.912522image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.14748725 2
 
< 0.1%
1.345886475 2
 
< 0.1%
0.7085602404 2
 
< 0.1%
2.24216869 2
 
< 0.1%
1.303940559 2
 
< 0.1%
4.211880961 2
 
< 0.1%
4.664080904 2
 
< 0.1%
8.678312755 2
 
< 0.1%
0.643174778 2
 
< 0.1%
2.604312336 2
 
< 0.1%
Other values (94703) 94795
> 99.9%
ValueCountFrequency (%)
0.007646571605 1
< 0.1%
0.01537414325 1
< 0.1%
0.01703500572 1
< 0.1%
0.01994969235 1
< 0.1%
0.02541720017 1
< 0.1%
0.02546122988 1
< 0.1%
0.02558615768 1
< 0.1%
0.02854700518 1
< 0.1%
0.02911750986 1
< 0.1%
0.03200618324 1
< 0.1%
ValueCountFrequency (%)
415.7525844 1
< 0.1%
14.15881423 1
< 0.1%
14.15799678 1
< 0.1%
14.15776049 1
< 0.1%
14.1499303 1
< 0.1%
14.1436442 1
< 0.1%
14.13952592 1
< 0.1%
14.13862493 1
< 0.1%
14.13033589 1
< 0.1%
14.12645899 1
< 0.1%

distancia_metro
Real number (ℝ)

SKEWED 

Distinct94445
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.48091602
Minimum0.0014160887
Maximum399.47737
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size740.9 KiB
2024-02-17T18:08:59.015778image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0.0014160887
5-th percentile0.09605202
Q10.21345803
median0.33188241
Q30.52302509
95-th percentile1.2179369
Maximum399.47737
Range399.47595
Interquartile range (IQR)0.30956706

Descriptive statistics

Standard deviation1.4335841
Coefficient of variation (CV)2.9809448
Kurtosis63289.197
Mean0.48091602
Median Absolute Deviation (MAD)0.14007542
Skewness227.86173
Sum45598.052
Variance2.0551634
MonotonicityNot monotonic
2024-02-17T18:08:59.113104image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.07262400891 3
 
< 0.1%
0.01007800048 3
 
< 0.1%
0.1351915238 3
 
< 0.1%
0.03547740611 3
 
< 0.1%
0.1322873217 2
 
< 0.1%
0.1530355285 2
 
< 0.1%
0.1938655324 2
 
< 0.1%
0.1763085068 2
 
< 0.1%
0.08111758685 2
 
< 0.1%
0.2642901621 2
 
< 0.1%
Other values (94435) 94791
> 99.9%
ValueCountFrequency (%)
0.001416088655 2
< 0.1%
0.002588903776 1
< 0.1%
0.003159330755 1
< 0.1%
0.004017688228 1
< 0.1%
0.004132945902 1
< 0.1%
0.004134038551 1
< 0.1%
0.004135130912 1
< 0.1%
0.004376046082 1
< 0.1%
0.004477056832 1
< 0.1%
0.004687972017 1
< 0.1%
ValueCountFrequency (%)
399.4773665 1
< 0.1%
9.42521385 1
< 0.1%
9.374053603 1
< 0.1%
9.355168276 1
< 0.1%
9.344541354 1
< 0.1%
9.341095837 1
< 0.1%
9.334654359 1
< 0.1%
9.329856913 1
< 0.1%
9.329834263 1
< 0.1%
8.98225003 1
< 0.1%

distancia_castellana
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct94707
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6792269
Minimum0.0014350974
Maximum412.80369
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size740.9 KiB
2024-02-17T18:08:59.230501image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0.0014350974
5-th percentile0.27503173
Q11.0351427
median1.9569777
Q33.8406409
95-th percentile7.1674747
Maximum412.80369
Range412.80225
Interquartile range (IQR)2.8054982

Descriptive statistics

Standard deviation2.581346
Coefficient of variation (CV)0.96346672
Kurtosis6720.2972
Mean2.6792269
Median Absolute Deviation (MAD)1.1877106
Skewness43.087906
Sum254030.9
Variance6.6633471
MonotonicityNot monotonic
2024-02-17T18:08:59.327830image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.034770529 2
 
< 0.1%
4.097475928 2
 
< 0.1%
5.539406549 2
 
< 0.1%
7.647690273 2
 
< 0.1%
0.03652147671 2
 
< 0.1%
0.781938254 2
 
< 0.1%
0.2266413713 2
 
< 0.1%
2.872269636 2
 
< 0.1%
2.328915683 2
 
< 0.1%
4.344945436 2
 
< 0.1%
Other values (94697) 94795
> 99.9%
ValueCountFrequency (%)
0.001435097407 1
< 0.1%
0.004269475612 1
< 0.1%
0.004322044394 1
< 0.1%
0.004934849361 1
< 0.1%
0.006173318995 1
< 0.1%
0.006305782332 1
< 0.1%
0.007929002888 1
< 0.1%
0.008344202002 1
< 0.1%
0.008422856558 1
< 0.1%
0.008501845916 1
< 0.1%
ValueCountFrequency (%)
412.8036884 1
< 0.1%
12.57782208 1
< 0.1%
12.57656576 1
< 0.1%
12.57358266 1
< 0.1%
12.5640167 1
< 0.1%
12.55972806 1
< 0.1%
12.55785999 1
< 0.1%
12.5537872 1
< 0.1%
12.55331639 1
< 0.1%
12.5372153 1
< 0.1%

longitud
Real number (ℝ)

Distinct94713
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-3.6864076
Minimum-3.8336106
Maximum-2.7533027
Zeros0
Zeros (%)0.0%
Negative94815
Negative (%)100.0%
Memory size740.9 KiB
2024-02-17T18:08:59.428991image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-3.8336106
5-th percentile-3.7475306
Q1-3.7084745
median-3.6941041
Q3-3.6662268
95-th percentile-3.6120087
Maximum-2.7533027
Range1.0803079
Interquartile range (IQR)0.042247671

Descriptive statistics

Standard deviation0.039239495
Coefficient of variation (CV)-0.010644372
Kurtosis4.0039309
Mean-3.6864076
Median Absolute Deviation (MAD)0.020845078
Skewness0.54334525
Sum-349526.74
Variance0.001539738
MonotonicityNot monotonic
2024-02-17T18:08:59.534700image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-3.668397562 2
 
< 0.1%
-3.707368374 2
 
< 0.1%
-3.704031569 2
 
< 0.1%
-3.677467904 2
 
< 0.1%
-3.702816896 2
 
< 0.1%
-3.662555834 2
 
< 0.1%
-3.708484583 2
 
< 0.1%
-3.779258617 2
 
< 0.1%
-3.697557458 2
 
< 0.1%
-3.677426773 2
 
< 0.1%
Other values (94703) 94795
> 99.9%
ValueCountFrequency (%)
-3.833610625 1
< 0.1%
-3.833107748 1
< 0.1%
-3.832932814 1
< 0.1%
-3.832706688 1
< 0.1%
-3.832533869 1
< 0.1%
-3.832513838 1
< 0.1%
-3.832442618 1
< 0.1%
-3.832430377 1
< 0.1%
-3.827888505 1
< 0.1%
-3.827372412 1
< 0.1%
ValueCountFrequency (%)
-2.75330272 1
< 0.1%
-3.540813685 1
< 0.1%
-3.540837846 1
< 0.1%
-3.540910906 1
< 0.1%
-3.541013201 1
< 0.1%
-3.54102911 1
< 0.1%
-3.541071002 1
< 0.1%
-3.541099714 1
< 0.1%
-3.541146808 1
< 0.1%
-3.541271311 1
< 0.1%

latitud
Real number (ℝ)

Distinct94713
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.421083
Minimum36.756391
Maximum40.520637
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size740.9 KiB
2024-02-17T18:08:59.635138image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum36.756391
5-th percentile40.367328
Q140.396868
median40.423295
Q340.441998
95-th percentile40.476588
Maximum40.520637
Range3.7642454
Interquartile range (IQR)0.045130389

Descriptive statistics

Standard deviation0.035495222
Coefficient of variation (CV)0.00087813633
Kurtosis1197.6169
Mean40.421083
Median Absolute Deviation (MAD)0.022747958
Skewness-11.57701
Sum3832525
Variance0.0012599108
MonotonicityNot monotonic
2024-02-17T18:08:59.750962image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.42515983 2
 
< 0.1%
40.42836941 2
 
< 0.1%
40.41022755 2
 
< 0.1%
40.41861152 2
 
< 0.1%
40.42828005 2
 
< 0.1%
40.43771601 2
 
< 0.1%
40.45833551 2
 
< 0.1%
40.46930339 2
 
< 0.1%
40.41329384 2
 
< 0.1%
40.42861179 2
 
< 0.1%
Other values (94703) 94795
> 99.9%
ValueCountFrequency (%)
36.7563914 1
< 0.1%
40.32868222 1
< 0.1%
40.32870614 1
< 0.1%
40.33165199 1
< 0.1%
40.33169949 1
< 0.1%
40.33212122 1
< 0.1%
40.33212891 1
< 0.1%
40.33214619 1
< 0.1%
40.33222099 1
< 0.1%
40.33231457 1
< 0.1%
ValueCountFrequency (%)
40.52063684 1
< 0.1%
40.52034852 1
< 0.1%
40.5202431 1
< 0.1%
40.52006389 1
< 0.1%
40.51987313 1
< 0.1%
40.51986685 1
< 0.1%
40.51983257 1
< 0.1%
40.51975356 1
< 0.1%
40.51959642 1
< 0.1%
40.51946819 1
< 0.1%

ciudad
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size740.9 KiB
Madrid
94815 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters568890
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMadrid
2nd rowMadrid
3rd rowMadrid
4th rowMadrid
5th rowMadrid

Common Values

ValueCountFrequency (%)
Madrid 94815
100.0%

Length

2024-02-17T18:08:59.850985image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T18:08:59.913848image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
madrid 94815
100.0%

Most occurring characters

ValueCountFrequency (%)
d 189630
33.3%
M 94815
16.7%
a 94815
16.7%
r 94815
16.7%
i 94815
16.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 474075
83.3%
Uppercase Letter 94815
 
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 189630
40.0%
a 94815
20.0%
r 94815
20.0%
i 94815
20.0%
Uppercase Letter
ValueCountFrequency (%)
M 94815
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 568890
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 189630
33.3%
M 94815
16.7%
a 94815
16.7%
r 94815
16.7%
i 94815
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 568890
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 189630
33.3%
M 94815
16.7%
a 94815
16.7%
r 94815
16.7%
i 94815
16.7%

Interactions

2024-02-17T18:08:49.150215image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:21.277449image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:22.896986image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:24.592763image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:27.659121image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:29.055630image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:30.609042image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:31.989714image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:33.380721image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:34.756380image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:36.221356image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:37.673315image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:39.136994image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-02-17T18:08:37.239365image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:38.724113image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:40.105129image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:41.569731image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:43.054161image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:47.268496image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:48.754125image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:50.300317image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:22.518372image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:24.103714image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:27.275823image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:28.739071image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:30.239776image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:31.658532image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:33.037628image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:34.421229image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:35.871515image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:37.321058image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:38.800349image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:40.194519image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:41.636114image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:43.135813image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:47.334346image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:48.819007image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:50.402592image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:22.626255image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:24.296487image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:27.374454image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:28.819715image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:30.325039image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:31.739071image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:33.122730image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:34.504008image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:35.974001image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:37.406467image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:38.889533image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:40.275541image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:41.723218image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:43.235632image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:47.434192image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:48.919278image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:50.483326image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:22.712238image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:24.392859image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:27.461793image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:28.893394image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:30.405447image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:31.825918image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:33.191499image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:34.588010image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:36.037861image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:37.496426image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:38.970774image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:40.353207image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:41.806933image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:46.119600image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:47.500775image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:48.987559image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:50.569134image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:22.812081image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:24.491925image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:27.541352image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:28.975578image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:30.511427image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:31.904714image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:33.274989image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:34.673203image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:36.138206image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:37.572322image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:39.054850image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:40.439059image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:41.906995image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:46.201326image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:47.601010image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-17T18:08:49.066953image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Correlations

2024-02-17T18:08:59.998396image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
a_reformaraire_acondicionadoamuebladoano_construccionarea_construidaaricoarmariosascensorbuen_estadocat_ano_construccioncat_calidadcat_n_max_pisoscat_n_vecinosdistancia_castellanadistancia_metrodistancia_puerta_solduplexestudioexterior_interiorfechajardinlatitudlongitudn_banosn_habitacionesn_pisonueva_construccionorientacion_eorientacion_norientacion_oorientacion_sparkingparking_incluido_preciopiscinaporteroprecioprecio_parkingprecio_unitario_km2terrazatrastero
a_reformar1.0000.2310.061-0.2230.0680.0030.1520.0190.910-0.1640.0040.052-0.023-0.111-0.083-0.1050.0500.0400.0300.0020.1130.050-0.034-0.0080.1800.0230.0850.0260.0290.0410.0070.1150.1150.1470.0500.020-0.008-0.0340.0360.077
aire_acondicionado0.2311.0000.0430.1960.1240.0600.3490.1560.2760.123-0.1500.0550.077-0.0420.0130.0030.0740.0240.0550.0340.1480.0720.0370.152-0.0380.1250.1380.0960.0560.0890.1080.1750.1750.1440.1710.2150.0560.1910.0570.139
amueblado0.0610.0431.000-0.024-0.0210.0060.0480.0120.064-0.0260.0020.006-0.005-0.027-0.022-0.0320.0060.0090.0290.0310.0270.003-0.017-0.016-0.007-0.0020.0460.0200.0080.0070.0240.0280.0280.0220.035-0.0080.0260.0080.0210.033
ano_construccion-0.2230.196-0.0241.0000.1570.0000.0080.0000.0120.992-0.0650.0430.2060.4110.3750.5480.0000.0000.0000.0080.0180.0300.1730.168-0.0270.0740.0000.0100.0040.0000.0000.0160.0160.0140.000-0.0110.087-0.2050.0100.008
area_construida0.0680.124-0.0210.1571.0000.0260.1480.2820.0960.159-0.3650.2740.150-0.0660.0550.0310.1040.0770.1670.0300.1620.1760.0510.7750.7180.2090.0530.0600.0470.0950.0920.2710.2710.1750.3260.7250.0620.1290.1320.272
arico0.0030.0600.0060.0000.0261.0000.0320.0000.0070.015-0.013-0.028-0.025-0.0110.000-0.0090.0890.0000.0190.0160.0100.009-0.0070.008-0.0090.1440.0270.0340.0300.0420.0440.0190.0190.0080.0300.0300.0080.0250.0900.025
armarios0.1520.3490.0480.0080.1480.0321.0000.2580.2210.146-0.1900.1450.143-0.0460.0140.0050.0440.0310.0560.0110.1910.1100.0220.2210.0520.1020.1860.1330.0940.1160.1610.1890.1890.1350.2400.2680.0790.1830.1280.159
ascensor0.0190.1560.0120.0000.2820.0000.2581.0000.0300.208-0.3880.4440.385-0.1160.004-0.0140.0210.0210.0530.0240.2440.2170.0820.3890.1580.2180.1150.0630.0530.0700.0570.2950.2950.2590.3500.5270.0740.3730.0740.240
buen_estado0.9100.2760.0640.0120.0960.0070.2210.0301.0000.0960.019-0.0560.0110.0600.0330.0470.0430.0450.0000.0110.040-0.0380.010-0.043-0.169-0.0190.3360.0120.0020.0090.0320.0340.0340.0200.003-0.0420.0110.0330.0580.003
cat_ano_construccion-0.1640.123-0.0260.9920.1590.0150.1460.2080.0961.000-0.1210.0650.2400.3960.3710.5210.1010.0650.2580.0150.3770.0060.1680.1650.0260.0710.1340.0140.0170.0050.0230.4250.4250.4440.101-0.0210.041-0.2110.2030.291
cat_calidad0.004-0.1500.002-0.065-0.365-0.013-0.190-0.3880.019-0.1211.000-0.312-0.3610.3050.0850.2400.0440.0430.1100.0250.223-0.212-0.010-0.403-0.097-0.1390.1160.0440.0330.0670.0500.2960.2960.3110.328-0.593-0.037-0.5310.0750.254
cat_n_max_pisos0.0520.0550.0060.0430.274-0.0280.1450.444-0.0560.065-0.3121.0000.627-0.156-0.028-0.0440.0510.0440.1350.0100.1890.1280.0880.2700.1730.3020.1010.0410.0380.0550.0370.1670.1670.1790.3200.3620.0310.2600.1260.125
cat_n_vecinos-0.0230.077-0.0050.2060.150-0.0250.1430.3850.0110.240-0.3610.6271.0000.0320.0720.1310.0040.0110.0610.0060.2680.0860.1430.1850.0630.2040.0690.0030.0100.0110.0020.2280.2280.3280.0990.2210.0200.1570.0160.171
distancia_castellana-0.111-0.042-0.0270.411-0.066-0.011-0.046-0.1160.0600.3960.305-0.1560.0321.0000.3670.7140.0000.0000.0000.0060.000-0.2530.176-0.1040.050-0.0410.0000.0000.0000.0000.0000.0000.0000.0000.000-0.435-0.040-0.6050.0000.000
distancia_metro-0.0830.013-0.0220.3750.0550.0000.0140.0040.0330.3710.085-0.0280.0720.3671.0000.4390.0000.0000.0000.0060.000-0.0850.0560.0440.065-0.0020.0000.0000.0000.0000.0000.0000.0000.0000.000-0.173-0.016-0.3230.0000.000
distancia_puerta_sol-0.1050.003-0.0320.5480.031-0.0090.005-0.0140.0470.5210.240-0.0440.1310.7140.4391.0000.0000.0000.0000.0060.0000.1120.3730.0040.0990.0190.0000.0000.0000.0000.0000.0000.0000.0000.000-0.312-0.032-0.5260.0000.000
duplex0.0500.0740.0060.0000.1040.0890.0440.0210.0430.1010.0440.0510.0040.0000.0000.0001.0000.0300.0150.0030.0470.041-0.0010.106-0.002-0.0120.0100.0120.0030.0160.0250.0640.0640.0550.0130.0660.0180.0100.0530.051
estudio0.0400.0240.0090.0000.0770.0000.0310.0210.0450.0650.0430.0440.0110.0000.0000.0000.0301.0000.0850.0090.0240.026-0.020-0.118-0.294-0.0800.0160.0120.0000.0100.0000.0350.0350.0200.014-0.0960.0120.0760.0730.046
exterior_interior0.0300.0550.0290.0000.1670.0190.0560.0530.0000.2580.1100.1350.0610.0000.0000.0000.0150.0851.0000.0270.1580.014-0.067-0.197-0.258-0.1230.0660.0350.0360.0400.0560.1670.1670.1400.034-0.075-0.0200.2130.1880.162
fecha0.0020.0340.0310.0080.0300.0160.0110.0240.0110.0150.0250.0100.0060.0060.0060.0060.0030.0090.0271.0000.0260.0220.0080.001-0.0160.0020.0180.0220.0160.0320.0170.0320.0320.0240.0130.0420.0140.0640.0160.033
jardin0.1130.1480.0270.0180.1620.0100.1910.2440.0400.3770.2230.1890.2680.0000.0000.0000.0470.0240.1580.0261.0000.1050.1310.2570.0990.0770.1600.0590.0620.0560.0710.4810.4810.6700.2360.1830.0310.0070.1420.361
latitud0.0500.0720.0030.0300.1760.0090.1100.217-0.0380.006-0.2120.1280.086-0.253-0.0850.1120.0410.0260.0140.0220.1051.0000.1690.1960.0450.0380.0000.0000.0000.0000.0000.0000.0000.0000.0000.4570.0280.4500.0000.000
longitud-0.0340.037-0.0170.1730.051-0.0070.0220.0820.0100.168-0.0100.0880.1430.1760.0560.373-0.001-0.020-0.0670.0080.1310.1691.0000.0670.0220.0390.1610.0000.0080.0180.0140.1880.1880.2490.1020.016-0.007-0.0380.1090.144
n_banos-0.0080.152-0.0160.1680.7750.0080.2210.389-0.0430.165-0.4030.2700.185-0.1040.0440.0040.106-0.118-0.1970.0010.2570.1960.0671.0000.5630.1520.0000.0470.0440.0680.0690.2070.2070.1170.2610.6710.0580.2680.1080.205
n_habitaciones0.180-0.038-0.007-0.0270.718-0.0090.0520.158-0.1690.026-0.0970.1730.0630.0500.0650.099-0.002-0.294-0.258-0.0160.0990.0450.0220.5631.0000.1770.0000.0000.0000.0100.0000.0060.0060.0120.0080.3950.013-0.0910.0040.005
n_piso0.0230.125-0.0020.0740.2090.1440.1020.218-0.0190.071-0.1390.3020.204-0.041-0.0020.019-0.012-0.080-0.1230.0020.0770.0380.0390.1520.1771.0000.0280.0470.0560.0670.0560.1330.1330.1150.1950.2280.0220.1400.1930.095
nueva_construccion0.0850.1380.0460.0000.0530.0270.1860.1150.3360.1340.1160.1010.0690.0000.0000.0000.0100.0160.0660.0180.1600.0000.1610.0000.0000.0281.0000.0880.0580.0720.0940.1780.1780.2850.1020.055-0.008-0.0020.0560.187
orientacion_e0.0260.0960.0200.0100.0600.0340.1330.0630.0120.0140.0440.0410.0030.0000.0000.0000.0120.0120.0350.0220.0590.0000.0000.0470.0000.0470.0881.0000.0880.1020.1150.0400.0400.0160.0910.0840.0360.0430.0720.051
orientacion_n0.0290.0560.0080.0040.0470.0300.0940.0530.0020.0170.0330.0380.0100.0000.0000.0000.0030.0000.0360.0160.0620.0000.0080.0440.0000.0560.0580.0881.0000.1160.0490.0450.0450.0350.0560.0700.0150.0370.0570.039
orientacion_o0.0410.0890.0070.0000.0950.0420.1160.0700.0090.0050.0670.0550.0110.0000.0000.0000.0160.0100.0400.0320.0560.0000.0180.0680.0100.0670.0720.1020.1161.0000.0620.0470.0470.0210.0880.1170.0360.0710.0620.061
orientacion_s0.0070.1080.0240.0000.0920.0440.1610.0570.0320.0230.0500.0370.0020.0000.0000.0000.0250.0000.0560.0170.0710.0000.0140.0690.0000.0560.0940.1150.0490.0621.0000.0500.0500.0280.0950.1040.0260.0550.0800.058
parking0.1150.1750.0280.0160.2710.0190.1890.2950.0340.4250.2960.1670.2280.0000.0000.0000.0640.0350.1670.0320.4810.0000.1880.2070.0060.1330.1780.0400.0450.0470.0501.0001.0000.5450.2340.293-0.0820.0880.1320.450
parking_incluido_precio0.1150.1750.0280.0160.2710.0190.1890.2950.0340.4250.2960.1670.2280.0000.0000.0000.0640.0350.1670.0320.4810.0000.1880.2070.0060.1330.1780.0400.0450.0470.0501.0001.0000.5450.2340.293-0.0820.0880.1320.450
piscina0.1470.1440.0220.0140.1750.0080.1350.2590.0200.4440.3110.1790.3280.0000.0000.0000.0550.0200.1400.0240.6700.0000.2490.1170.0120.1150.2850.0160.0350.0210.0280.5450.5451.0000.2280.2290.0150.0780.0920.417
portero0.0500.1710.0350.0000.3260.0300.2400.3500.0030.1010.3280.3200.0990.0000.0000.0000.0130.0140.0340.0130.2360.0000.1020.2610.0080.1950.1020.0910.0560.0880.0950.2340.2340.2281.0000.4280.0660.2950.0760.175
precio0.0200.215-0.008-0.0110.7250.0300.2680.527-0.042-0.021-0.5930.3620.221-0.435-0.173-0.3120.066-0.096-0.0750.0420.1830.4570.0160.6710.3950.2280.0550.0840.0700.1170.1040.2930.2930.2290.4281.0000.0790.7460.0680.180
precio_parking-0.0080.0560.0260.0870.0620.0080.0790.0740.0110.041-0.0370.0310.020-0.040-0.016-0.0320.0180.012-0.0200.0140.0310.028-0.0070.0580.0130.022-0.0080.0360.0150.0360.026-0.082-0.0820.0150.0660.0791.0000.0520.0000.012
precio_unitario_km2-0.0340.1910.008-0.2050.1290.0250.1830.3730.033-0.211-0.5310.2600.157-0.605-0.323-0.5260.0100.0760.2130.0640.0070.450-0.0380.268-0.0910.140-0.0020.0430.0370.0710.0550.0880.0880.0780.2950.7460.0521.0000.1420.135
terraza0.0360.0570.0210.0100.1320.0900.1280.0740.0580.2030.0750.1260.0160.0000.0000.0000.0530.0730.1880.0160.1420.0000.1090.1080.0040.1930.0560.0720.0570.0620.0800.1320.1320.0920.0760.0680.0000.1421.0000.119
trastero0.0770.1390.0330.0080.2720.0250.1590.2400.0030.2910.2540.1250.1710.0000.0000.0000.0510.0460.1620.0330.3610.0000.1440.2050.0050.0950.1870.0510.0390.0610.0580.4500.4500.4170.1750.1800.0120.1350.1191.000

Missing values

2024-02-17T18:08:50.766237image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-17T18:08:51.333606image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

id_anunciofechaprecioprecio_unitario_km2tipologia_imuebleoperacionarea_construidan_habitacionesn_banosterrazaascensoraire_acondicionadoamuebladoparkingparking_incluido_precioprecio_parkingorientacion_norientacion_sorientacion_eorientacion_otrasteroarmariospiscinaporterojardinduplexestudioaricoano_construccionn_pisoexterior_interiorcat_ano_construccioncat_n_max_pisoscat_n_vecinoscat_calidadnueva_construcciona_reformarbuen_estadodistancia_puerta_soldistancia_metrodistancia_castellanalongitudlatitudciudad
0A150191368314062380292018031260002680.851064HOMESALE471101130010000111110002005.01.01.0200573193.00108.0584290.8720756.868677-3.76693340.362485Madrid
1A66772259054720653442018032350004351.851852HOMESALE54110003001000001000000NaN1.02.019005113.00010.8763690.1163821.544125-3.71072540.422430Madrid
2A133419797486185247752018033730004973.333333HOMESALE75210013001010011000000NaN3.01.019156263.00010.9074790.1391091.608444-3.71157140.422190Madrid
3A47751821756152765422018032840005916.666667HOMESALE48110113001000000000000NaN1.02.019479155.00010.8454620.1442991.516166-3.71044040.422251Madrid
4A24920877307117019732018032280004560.000000HOMESALE500100030010000000000101930.00.01.019305197.00011.2502310.3370981.794136-3.71434040.408741Madrid
5A183724281546811114192018034980003921.259843HOMESALE127320103001000000000000NaN3.01.019005183.00100.5417730.1614361.168126-3.70752240.412639Madrid
6A47059464107954640362018032250006428.571429HOMESALE35010103001000000000010NaN2.02.019426153.00010.8595650.1269951.517437-3.71039540.422450Madrid
7A82437625374777817182018033650003650.000000HOMESALE100211103001000000010000NaN4.01.019606266.00011.3461150.2634451.762922-3.71412640.407409Madrid
8A95874495076286580132018034250006071.428571HOMESALE701101011110001000000001900.02.01.019005161.00010.7535750.4371911.548310-3.71239040.414870Madrid
9A369430051833770296720180331870008852.777778HOMESALE360431113111111111010000NaN9.01.019727153.00010.7792280.3126161.609466-3.71294540.417236Madrid
id_anunciofechaprecioprecio_unitario_km2tipologia_imuebleoperacionarea_construidan_habitacionesn_banosterrazaascensoraire_acondicionadoamuebladoparkingparking_incluido_precioprecio_parkingorientacion_norientacion_sorientacion_eorientacion_otrasteroarmariospiscinaporterojardinduplexestudioaricoano_construccionn_pisoexterior_interiorcat_ano_construccioncat_n_max_pisoscat_n_vecinoscat_calidadnueva_construcciona_reformarbuen_estadodistancia_puerta_soldistancia_metrodistancia_castellanalongitudlatitudciudad
94805A69058554622807808252018121740002047.058824HOMESALE850201131110001000011102007.01.01.020073675.00019.5039150.2943558.392438-3.59313340.402823Madrid
94806A70348514354654528922018122290001908.333333HOMESALE1203211131110001010011002010.00.01.020053675.00019.4940480.2700048.380544-3.59329640.402615Madrid
94807A103248532191208995442018122930002738.317757HOMESALE1073201131110001111010002004.05.01.020048207.00018.4762470.5014797.327070-3.60613140.400215Madrid
94808A89744858193015334632018121960005939.393939HOMESALE33010113111000001100010NaN2.01.02008114.00016.5498430.5548904.857431-3.63143640.437287Madrid
94809A88706644643017376772018123380003281.553398HOMESALE103211113111100001001000NaN3.01.019985744.00018.7591080.5088777.374166-3.60172140.428977Madrid
94810A39621867994789401772018123470003017.391304HOMESALE115321113111000011101000NaN1.01.020097583.000110.0030590.8668268.276950-3.59215440.445810Madrid
94811A38761643670537817292018123110003344.086022HOMESALE932201131111100111010002007.02.01.0200771613.000110.1981471.0197888.496364-3.58937640.445013Madrid
94812A177682957867724926982018033420002826.446281HOMESALE1212211121110010111010002005.03.01.0200961073.000111.2040271.8836509.573127-3.57627140.443196Madrid
94813A157331446493596686352018061460002354.838710HOMESALE623100020010000010000001970.03.02.019704175.00018.7806920.1717366.941217-3.60869440.447931Madrid
94814A97164883928390161172018123340003591.397849HOMESALE933111130011010011010001972.02.01.0197493015.000111.0704170.2123778.799697-3.58505640.458098Madrid